EP3127087B1 - Estimation d'un champ de demouvement - Google Patents

Estimation d'un champ de demouvement Download PDF

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EP3127087B1
EP3127087B1 EP15715202.6A EP15715202A EP3127087B1 EP 3127087 B1 EP3127087 B1 EP 3127087B1 EP 15715202 A EP15715202 A EP 15715202A EP 3127087 B1 EP3127087 B1 EP 3127087B1
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reference frame
frame
quality
motion
current
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EP3127087A1 (fr
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Philippe Robert
Tomas Enrique CRIVELLI
Pierre-Henri Conze
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Thomson Licensing SAS
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G06T7/246—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/102—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or selection affected or controlled by the adaptive coding
    • H04N19/103—Selection of coding mode or of prediction mode
    • H04N19/105—Selection of the reference unit for prediction within a chosen coding or prediction mode, e.g. adaptive choice of position and number of pixels used for prediction
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/134—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or criterion affecting or controlling the adaptive coding
    • H04N19/136—Incoming video signal characteristics or properties
    • H04N19/137—Motion inside a coding unit, e.g. average field, frame or block difference
    • H04N19/139—Analysis of motion vectors, e.g. their magnitude, direction, variance or reliability
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/134—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or criterion affecting or controlling the adaptive coding
    • H04N19/154—Measured or subjectively estimated visual quality after decoding, e.g. measurement of distortion
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/169—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding
    • H04N19/17—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object
    • H04N19/172—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a picture, frame or field
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/50—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding
    • H04N19/503—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding involving temporal prediction
    • H04N19/51—Motion estimation or motion compensation
    • H04N19/513—Processing of motion vectors
    • H04N19/521—Processing of motion vectors for estimating the reliability of the determined motion vectors or motion vector field, e.g. for smoothing the motion vector field or for correcting motion vectors
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/50—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding
    • H04N19/503—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding involving temporal prediction
    • H04N19/51—Motion estimation or motion compensation
    • H04N19/58—Motion compensation with long-term prediction, i.e. the reference frame for a current frame not being the temporally closest one
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10016—Video; Image sequence
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10024—Color image
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30168—Image quality inspection

Definitions

  • the present invention relates generally to the field of video processing. More precisely, the invention relates to a method and a device for generating motion fields for a video sequence with respect to a reference frame.
  • the propagation of information requires motion correspondence between the reference frame and the other frames of the sequence.
  • a first method for generating motion fields consists in performing a direct matching between the considered frames, ie the reference frame and a current frame.
  • the motion range is generally very large and estimation can be very sensitive to ambiguous correspondences, like for instance, within periodic image patterns.
  • a second method consists in obtaining motion estimation through sequential concatenation of elementary optical flow fields. These elementary optical flow fields can be computed between consecutive frames and are relatively accurate. However, this strategy is very sensitive to motion errors as one erroneous motion vector is enough to make the concatenated motion vector wrong. It becomes very critical in particular when concatenation involves a high number of elementary vectors.
  • state-of-the-art dense motion trackers process the sequence sequentially in a frame-by-frame manner, and associate, by design, features that disappear (occlusion) and reappear in the video, with different tracks, thereby losing important information of the long-term motion signal.
  • occlusions along the sequence or erroneous motion correspondences raise the issue of the quality of the propagation between distant frames. In other words, the length of good tracking depends on the scene content.
  • Rubinstein et al. disclose an algorithm that re-correlates short trajectories, called “tracklets”, estimated with respect to different starting frames and links them to form a long-range motion representation. To that end, Rubinstein et al. tend to go towards longer long-range motion trajectories. If they appear to connect tracklets, especially cut by an occlusion, the method remains limited to sparse motion trajectories.
  • the international patent application WO2013107833 discloses a method for generating long term motion fields between a reference frame and each of the other frames of a video sequence.
  • the reference frame is for example the first frame of the video sequence.
  • the method consists in sequential motion estimation between the reference frame and the current frame, this current frame being successively the frame adjacent to the reference frame, then the next one and so on.
  • the method relies on various input elementary motion fields that are supposed to be pre-computed. These motion fields link pairs of frames in the sequence with good quality as inter-frame motion range is supposed to be compatible with the motion estimator performance.
  • the current motion field estimation between the current frame and the reference frame relies on previously estimated motion fields (between the reference frame and frames preceding the current one) and elementary motion fields that link the current frame to the previous processed frames: various motion candidates are built by concatenating elementary motion fields and previous estimated motion fields. Then, these various candidate fields are merged to form the current output motion field. This method is a good sequential option but cannot avoid possible drifts in some pixels. Then, once an error is introduced in a motion field, it can be propagated to the next fields during the sequential processing.
  • a highly desirable functionality of a video editing application is to be able to determine a set of reference frames along the sequence in order for example to track an area defined by an operator, or propagate information initially assigned to this area by the operator.
  • a salient idea of the invention is to consider a quality measure that evaluates the quality of representation of a frame or a region by respectively another frame or a region in another frame in the video.
  • quality measure is used to introduce a new reference frame in very long-term dense motion estimation in a video sequence.
  • the basic idea behind this is to insert new reference frames along the sequence each time the motion estimation process fails and then to apply the motion estimator with respect to each of these new reference frames.
  • a new reference frame replaces the previous reference frame for image processing algorithm (such as motion field estimation).
  • such insertion of new reference frame based on quality metrics avoids the motion drift and enhance the single reference frame estimation issues by combining the displacement vectors with good quality among all the generated multi-reference displacement vectors.
  • quality measure is used to select a first reference frame in the video sequence wherein a target area in a frame selected by a user is better represented.
  • the "reference frame” terminology is ambiguous.
  • a reference frame in the point of view of user interaction and a reference frame considered as an algorithmic tool should be dissociated.
  • the user will insert the texture/logo in one single reference frame and run the multi-reference frames algorithm described hereinafter.
  • the new reference frames inserted according to the invention are an algorithmic way to perform a better motion estimation without any user interaction.
  • the user selected frame is called first frame, even if initially used as a reference frame in a search for a first reference frame.
  • Figure 1 illustrates steps of the method according to a first preferred embodiment.
  • motion estimation between a reference frame and a current frame of the sequence is processed sequentially starting from a first frame next to the reference frame and then moving away from it progressively from current frame to current frame.
  • a quality metric evaluates for each current frame the quality of correspondence between the current frame and the reference frame. When quality reaches a quality threshold, a new reference frame is selected among the previously processed current frames (for example the previous current frame). From now on motion estimation is carried out and assessed with respect to this new reference frame. Other new reference frames may be introduced along the sequence when processing the next current frames.
  • motion vectors of a current frame with respect to the first reference frame are obtained by concatenating the motion vectors of the current frame with successive motion vectors computed between pairs of reference frames up to reach the first reference frame.
  • the quality metric is normalized and defined in the interval [0,1], with the best quality corresponding to 1. According to this convention, a quality criterion is reached when the quality metric is above the quality threshold.
  • the current frame is initialized to as one of the two neighboring frames of the reference frame (if the reference frame is neither the first nor the last one), and then the next current frame is the neighboring frame of the current frame.
  • a motion field between the current frame and the reference frame is determined.
  • a motion field comprises for each pair of frames comprising a reference frame and a current frame, and for each pixel of the current frame, a corresponding point (called motion vector endpoint) in the reference frame.
  • Such correspondence is represented by a motion vector between the first pixel of the current frame and the corresponding point in the reference frame. In the particular case where the point is out of the camera field or occluded, such corresponding point does not exist.
  • a quality metric representative of the quality of the determined motion field is evaluated and compared to a motion quality threshold.
  • the quality metric is evaluated according to different variants using Figure 2 .
  • the quality metric is function of a mean of inconsistency values of a set of pixels of the reference frame.
  • An inconsistency value is the distance 20 between a first pixel X A in the reference frame 21 and a point 22 in the reference frame 21 corresponding to the endpoint of an inverse motion vector 23 from the endpoint X B into the current frame 24 of a motion vector 25 from the first pixel X A .
  • Forward 23 (resp. backward 25) motion field refers for example to the motion field that links the pixels of reference frame 21 (resp. current frame 24) to current frame 24 (resp. reference frame 21).
  • x A is the 2D position of a pixel while x B corresponds to the endpoint of motion vector D ( x A ) in the current frame.
  • this latter position does not correspond to a pixel.
  • D ( x B ) is estimated via bilinear interpolation from the vectors attached to the four neighbouring pixels 26 in a 2D representation.
  • the inconsistency values are binarized.
  • a binary inconsistency value is set (for instance to a value one) in the case where the distance between a first pixel X A in the reference frame 21 and a point 22 in the reference frame 21 corresponding to the endpoint of an inverse motion vector 23 from the endpoint X B into the current frame 24 of a motion vector 25 from the first pixel X A is above an inconsistency threshold.
  • the binary inconsistency value is reset (for instance set to zero) in the case where the distance is below an inconsistency threshold.
  • the quality metric comprises a normalized number of pixels among a set of pixels of the reference frame 21 whose binary inconsistency value is reset.
  • the quality metric is estimated using a matching cost representative of how accurately a first pixel X A of a reference frame 21 can be reconstructed by the matched point X B in the current frame.
  • a motion compensated absolute difference is computed between the endpoint X B into the current frame 24 of a motion vector 25 from a first pixel X A in the reference frame 21 and the first pixel X A in the reference frame 21.
  • the difference for instance, refers to the difference of the luminance value of the pixel in the RGB colour scheme.
  • this variant is compatible with any value representative of the pixel in the video as detailed above.
  • the quality metric is function of a mean of motion compensated absolute differences of a set of pixels of the reference frame.
  • the matching cost C( x A , D ) of pixel x A in the reference frame corresponds in this case to the sum on the 3 color channels RGB (corresponding to I C ) of absolute difference between the value at this pixel and the value at point ( x A - D ) in the current frame where D corresponds to the motion vector 25 with respect to current frame assigned to pixel x A .
  • quality metric a function of a peak signal-to-noise ratio of a set of pixels of the reference frame.
  • PSNR peak signal-to-noise ratio
  • MSE mean square error
  • an important information that must be considered to evaluate the quality of the representation of a first frame by a current frame is the number of pixels of the first frame with no correspondence in the current frame either because the scene point observed in first frame is occluded in current frame or because it is out of the camera field in the current frame.
  • Techniques exist to detect such pixels For example, figure 3 illustrates the method that consists in detecting possible pixels of first frame that have no correspondence in current frame (called occluded pixels) by projecting onto first frame 31 the motion field 33 of current frame 32 and marking the closest pixels to the endpoints in frame 31, and then identifying the pixels in frame 31 that are not marked. The more numerous the occluded pixels marked in frame 31 (i.e. pixels of frame 31 occluded in frame 32), the less representative frame 32 is for frame 31.
  • a global quality metric is defined in order to evaluate how accurately a current frame is globally well represented by a reference frame. For example, this global quality can result from counting the number of pixels which have a cost matching under a threshold, or counting the number of pixels which are "consistent" (i.e. which inconsistency distance is under an inconsistency threshold as in the second variant, i.e with a binary inconsistency value set to 0).
  • a proportion can then be derived with respect to the total number of visible pixels (that is pixels that are not occluded).
  • the proportion of visible pixels of current frame in reference frame can itself be a relevant parameter of how well current frame is represented by a reference frame.
  • these 'global' metric can also be computed on a particular area of interest indicated by the operator.
  • a weight can be introduced instead of a binary inconsistency value resulting from thresholding.
  • this weight can be given by the negative exponential function of the cost matching or of the inconsistency distance. Therefore, we propose the following quality measure of motion field in current frame with respect to reference frame:
  • Q D A / B ⁇ ⁇ A f C x ⁇ A , D ⁇ x A + ⁇ ⁇ A g I n c x ⁇ A , D ⁇ x ⁇ A
  • the quality metric is preferably defined in the interval [0,1], with the best quality corresponding to 1.
  • the invention is not limited to this convention.
  • a possible solution for f () and g () can be :
  • a new reference frame is determined in a step 12 among a group of previous current frames which have a quality metric above the quality threshold. Accordingly, the "to-the-reference" motion field (respectively vector) between the current frame and the reference frame is determined in a step 13 by concatenating (or summing) a motion field (respectively vector) between the current frame and the new reference frame and a motion field (respectively vector) between the new reference frame and the reference frame.
  • the "from-the-reference' motion field (respectively vector) between the reference frame and the current frame is determined in a step 13 by concatenating (or summing) a motion field (respectively vector) between the reference frame and the new reference frame and a motion field (respectively vector) between the new reference frame and the current frame.
  • the quality metric is below the quality threshold
  • the previous current frame in the sequential processing is selected as a new reference frame.
  • new pairs of frames are considered grouping this new reference frame and next current frames (not yet processed).
  • the correspondence between these frames and the reference frame is obtained by concatenation of the motion fields (respectively vectors).
  • the method can be carried out starting from first frame sequentially in any direction along the temporal axis.
  • the set of pixels used for determining the quality metric are comprised in a region of interest of the reference frame.
  • the selection of a new reference frame requires the candidate new reference frame to contain all the pixels of the reference area visible in the current frame.
  • direct motion estimation is carried out between the current frame and the reference frames in order to possibly select another reference. Actually, it may happen that the area of interest is temporarily occluded and becomes visible again after some frames.
  • T ( x ref 0 ) starts from the grid point x ref 0 of I ref 0 and is defined by a set of from-the-reference displacement vectors ⁇ d ref 0 ,n ( x ref 0 ) ⁇ ⁇ n ⁇ [ ref 0 + 1, ..., N ].
  • displacement vectors start from pixel x ref 0 (pixel they are assigned to) and point at each of the other frames n of the sequence.
  • the quality of T ( x ref 0 ) is estimated through the study of the binary inconsistency values assigned to each displacement vectors ⁇ d ref 0 ,n ( x ref 0 ) ⁇ ⁇ n ⁇ [ ref 0 + 1, ...,N ] . If one of these vectors is inconsistent, the process automatically adds a new reference frame at the instant which precedes the matching issue and runs the procedure described above.
  • the vector d ref 1 ,n ( x ref 0 + d ref 0 ,ref 1 ( x ref 0 )) can be computed via spatial bilinear interpolation.
  • the displacement selection criteria are more valid when we rely on a reference frame which is closer from the current frame than the initial reference frame ( I ref 0 ).
  • the matching can be more easily performed.
  • this multi reference frames motion estimation is enhaced compared to classic single reference frame approach.
  • a motion quality threshold must be set according to the quality requirements to determine from which instant a new reference frame is needed.
  • a local assessment which focuses only on the region of interest may be relevant when the whole images are not involved.
  • the quality of the motion estimation process highly depends on the area under consideration and studying the motion vector quality for the whole image could badly influence the reference frame insertion process in this case.
  • Figure 4 illustrates steps of the method according to a second preferred embodiment.
  • a first reference frame is determined for a user selected region of a first frame of the video sequence. For instance, given a video sequence, a user selects a particular frame either arbitrarily or according to a particular application that demands specific characteristics. Such user selected frame is, in the prior art, used as reference frame for any image processing algorithm. For example, if the user focuses his attention on a particular area he wants to edit, he may need this area to be totally visible in the reference frame. On the other hand, a region selected by the user in a frame may have a better resolution in another frame. Actually, this is not sure that the operator has selected the representation of the region along the video sequence with the finest resolution.
  • the invention advantageously allows that starting from this initial selection, a possible finer representation in the sequence is determined. This is done by identifying the corresponding region in the other frames, evaluating its size with respect to the size of the reference region.
  • the size of the regions is defined by their number of pixels.
  • the reference frame is initialized as the first frame (selected by the user), and a size threshold to the size of the user selected region in the first frame. Then the next current frame is the neighboring frame of the current frame.
  • a motion field between the first frame and the current frame is determined.
  • forward and backward motion fields are estimated between the first frame, used as reference frame, and the other current frames of the sequence. Those motion fields allow to identify the user selected region in the frames of the sequence.
  • motion field estimation is limited to the selected region of the reference frame. The estimation is obtained via pixel-wise or block-based motion estimation. The resulting dense motion field gives the correspondence between the pixels of the first frame and the pixels/points in each of the other current frames. If motion has a subpixel resolution, the pixel in the current frame corresponding to a given pixel X a of the first frame is identified as the closest one from the endpoint of the motion vector attached to pixel X A . Consequently, the region R B in current frame corresponding to the first region R A in the first frame is defined as the set of pixels that are the closest pixels with respect to the endpoints of the motion vectors attached to pixels of the first region.
  • a quality metric representative of the quality of the determined motion field between the first frame A and the current frame B is estimated.
  • the estimation is processed for the first region R A , defined by its set of pixels X A .
  • the motion fields should be reliable.
  • a motion quality metric is derived using for example one of the above variants. This measure noted Q D ( R A ,B ) is limited to the area of interest R A selected by the operator in first frame A.
  • Q D Q D ( R A , B ) is above a quality threshold it indicates that the area R B in current frame B corresponding to region R A is well identified.
  • another relevant parameter of the motion quality is the proportion of pixels of the first region R A visible in the current frame B (neither occluded nor out of the current frame).
  • This proportion noted O D ( R A , B ) must be also above a visibility threshold.
  • the visibility threshold is close to 1 so that most of the pixels of region R A are visible in current frame B, to be able to consider that R A can be represented by R B .
  • a size metric comprising a number of pixels in the region of the current frame corresponding to user selected region of the first frame is estimated.
  • this characteristic allows a comparison of the resolution of both corresponding regions R A and R B .
  • a variant consists in directly comparing the sizes of the regions, i.e. their number of pixels (called N A and N B ) : if N A > N B , then first region R A has a better resolution than region R B , otherwise identified region R B is a good candidate to better represent the area R A initially selected by the operator.
  • a fourth step 43 those two above metrics are tested.
  • the quality metric is higher a quality threshold
  • the size metric is higher than a size threshold
  • the first reference frame is set to the current frame and the size threshold updated with the size metric.
  • the steps are then sequentially iterated for each successive current frame of the sequence.
  • Figure 5 illustrates a device for processing a video sequence according to a particular embodiment of the invention.
  • the device is any device intended to process video bit-stream.
  • the device 400 comprises physical means intended to implement an embodiment of the invention, for instance a processor 501 (CPU or GPU), a data memory 502 (RAM, HDD), a program memory 503 (ROM), a man machine (MMI) interface 504 or a specific application adapted for the display of information for a user and/or the input of data or parameters (for example, a keyboard, a mouse, a touchscreen allowing a user to select and edit a frame..) and optionally a module 505 for implementation any of the function in hardware.
  • the data memory 502 stores the bit-stream representative of the video sequence, the sets of dense motion fields associated to the video sequence, program instructions that may be executable by the processor 501 to implement steps of the method described herein.
  • the generation of dense motion estimation is advantageously pre-computed for instance in the GPU or by a dedicated hardware module 505.
  • the processor 501 is configured to display the processed video sequence on a display device 504 attached to the processor.
  • the processor 501 is Graphic Processing Unit, coupled to a display device, allowing parallel processing of the video sequence thus reducing the computation time.
  • the processing method is implemented in a network cloud, i.e. in distributed processor connected through a network interface.
  • the program instructions may be provided to the device 500 via any suitable computer-readable storage medium.
  • a computer readable storage medium can take the form of a computer readable program product embodied in one or more computer readable medium(s) and having computer readable program code embodied thereon that is executable by a computer.
  • a computer readable storage medium as used herein is considered a non-transitory storage medium given the inherent capability to store the information therein as well as the inherent capability to provide retrieval of the information therefrom.
  • a computer readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing.

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Claims (15)

  1. Procédé pour générer des champs de mouvement pour des trames successives d'une séquence vidéo par rapport à une trame initialement sélectionnée comme trame de référence, le procédé comprenant :
    • la détermination (10) d'un champ de mouvement entre une trame courante (24) et ladite trame de référence initialement sélectionnée (21), à partir d'une trame courante adjacente à la trame de référence initialement sélectionnée, et l'évaluation d'une mesure de qualité représentative de la qualité du champ de mouvement déterminé ;
    • l'itération séquentielle desdites détermination et évaluation selon ladite trame de référence initialement sélectionnée pour des trames courantes successives tant que ladite mesure de qualité est supérieure à un seuil de qualité ;
    • la sélection d'une nouvelle trame de référence (12) parmi un groupe de trames courantes précédentes lorsque ladite mesure de qualité est inférieure audit seuil de qualité ; et
    • la détermination (13) d'un champ de mouvement entre la trame courante et ladite nouvelle trame de référence et la concaténation dudit champ de mouvement déterminé entre ladite trame courante et ladite nouvelle trame de référence avec un champ de mouvement précédemment généré entre ladite nouvelle trame de référence et ladite trame de référence initialement sélectionnée.
  2. Procédé selon la revendication 1, comprenant en outre :
    • l'évaluation d'une mesure de qualité représentative de la qualité du champ de mouvement déterminé entre ladite trame courante et ladite nouvelle trame de référence ;
    • l'itération séquentielle desdites détermination, concaténation et évaluation selon la nouvelle trame de référence sélectionnée pour des trames courantes successives tant que ladite mesure de qualité est supérieure à un seuil de qualité ; et
    • la sélection d'une autre nouvelle trame de référence (12) parmi un groupe de trames courantes précédentes lorsque la mesure de qualité est inférieure à un seuil de qualité.
  3. Procédé selon la revendication 1 ou 2, dans lequel une valeur d'incohérence est la distance (20) entre un premier pixel (XA) dans la trame de référence (21) et un point (22) dans la trame de référence (21) correspondant au point final d'un vecteur de mouvement inverse (23), associé à un champ de mouvement inverse entre ladite trame courante (24) et ladite trame de référence (21), à partir du point final (XB) dans ladite trame courante (24) d'un vecteur de mouvement (25) associé audit champ de mouvement déterminé à partir dudit premier pixel (XA) ; et dans lequel ladite mesure de qualité dépend d'une moyenne de valeurs d'incohérence d'un ensemble de pixels de ladite trame de référence.
  4. Procédé selon la revendication 1 ou 2, dans lequel une valeur d'incohérence binaire est définie sur une première valeur (1) si la distance (20) entre un premier pixel (XA) dans la trame de référence (21) et un point (22) dans la trame de référence (21) correspondant au point final d'un vecteur de mouvement inverse (23), associé à un champ de mouvement inverse entre ladite trame courante (24) et ladite trame de référence (21), à partir du point final (XB) dans ladite trame courante (24) d'un vecteur de mouvement (25) associé audit champ de mouvement déterminé à partir dudit premier pixel (XA) est supérieure à un seuil d'incohérence ; dans lequel ladite valeur d'incohérence binaire est définie sur une seconde valeur (0) si ladite distance est inférieure au seuil d'incohérence, et dans lequel ladite mesure de qualité est une proportion de pixels parmi un ensemble de pixels dont la valeur d'incohérence binaire est définie sur la seconde valeur (0).
  5. Procédé selon la revendication 1 ou 2, dans lequel une différence absolue compensée en mouvement est la différence absolue entre la couleur ou la luminance du point final (XB) dans ladite trame courante (24) d'un vecteur de mouvement (25) à partir d'un premier pixel (XA) de la trame de référence (21) et la couleur ou la luminance dudit premier pixel (XA) de ladite trame de référence (21), et dans lequel ladite mesure de qualité dépend d'une moyenne de différences absolues compensées en mouvement d'un ensemble de pixels de ladite trame de référence.
  6. Procédé selon la revendication 5, dans lequel ladite mesure de qualité comprend un rapport signal sur bruit de crête basé sur la moyenne de différences absolues compensées en mouvement d'un ensemble de pixels de ladite trame de référence.
  7. Procédé selon les revendications 3 et 5, dans lequel ladite mesure de qualité comprend une somme pondérée d'une fonction de la valeur d'incohérence et d'une fonction de la différence absolue compensée en mouvement.
  8. Procédé selon l'une quelconque des revendications 3 à 7, dans lequel ledit ensemble de pixels utilisé pour déterminer la mesure de qualité est compris dans une région d'intérêt de ladite trame de référence.
  9. Procédé selon l'une quelconque des revendications 1 à 8, dans lequel la sélection d'une nouvelle trame de référence parmi un groupe de trames courantes précédentes comprend la sélection de la trame courante précédente la plus proche de la trame courante.
  10. Procédé pour générer des champs de mouvement pour une séquence vidéo par rapport à une trame de référence selon l'une quelconque des revendications 1 à 9, dans lequel pour une région sélectionnée par l'utilisateur d'une trame de référence sélectionnée par l'utilisateur, le procédé comprend en outre la détermination comme ladite trame de référence d'une première trame de référence à partir de ladite trame de référence sélectionnée par l'utilisateur et de ladite région sélectionnée par l'utilisateur, pour chaque trame courante de la séquence vidéo, par :
    • la détermination (40) d'un champ de mouvement entre ladite trame courante et ladite trame de référence sélectionnée par l'utilisateur et la détermination (41) d'une mesure de qualité représentative de la qualité du champ de mouvement déterminé ;
    • la détermination (42) d'une mesure de taille comprenant un nombre de pixels dans la région de ladite trame courante correspondant à ladite région sélectionnée par l'utilisateur de ladite trame de référence sélectionnée par l'utilisateur ;
    • dans le cas (44) où ladite mesure de qualité est supérieure à un seuil de qualité et où ladite mesure de taille est supérieure à un seuil de taille, la sélection de ladite première trame de référence comme étant ladite trame courante et la définition du seuil de taille sur ladite mesure de taille déterminée, et
    l'utilisation de ladite première trame de référence comme ladite trame de référence lors de la génération des champs de mouvement pour la séquence vidéo.
  11. Procédé selon la revendication 10, dans lequel ledit seuil de taille est initialisé sur un nombre de pixels dans ladite région sélectionnée par l'utilisateur de ladite trame de référence sélectionnée par l'utilisateur.
  12. Procédé selon la revendication 10 ou 11, dans lequel la détermination de ladite mesure de qualité représentative de la qualité du champ de mouvement déterminé entre ladite trame de référence sélectionnée par l'utilisateur et ladite trame courante comprend en outre la détermination du nombre de pixels de la région sélectionnée par l'utilisateur de ladite trame de référence sélectionnée par l'utilisateur qui sont visibles dans la trame courante.
  13. Procédé selon l'une quelconque des revendications 10 ou 12, dans lequel la détermination de ladite mesure de qualité représentative de la qualité du champ de mouvement déterminé entre ladite trame de référence sélectionnée par l'utilisateur et ladite trame courante est limitée à ladite région sélectionnée par l'utilisateur de ladite trame de référence sélectionnée par l'utilisateur.
  14. Produit de programme informatique comprenant du code logiciel adapté pour exécuter le procédé selon l'une quelconque des revendications 1 à 13 lorsque le programme est exécuté par un processeur.
  15. Dispositif (500) pour générer des champs de mouvement pour une séquence vidéo par rapport à une trame initialement sélectionnée comme trame de référence, ledit dispositif comprenant au moins un processeur (501) configuré pour :
    • déterminer un champ de mouvement entre une trame courante (24) et ladite trame de référence initialement sélectionnée (21), à partir d'une trame courante adjacente à la trame de référence initialement sélectionnée, et évaluer une mesure de qualité représentative de la qualité du champ de mouvement déterminé ;
    • itérer de façon séquentielle lesdites détermination et évaluation selon ladite trame de référence initialement sélectionnée pour des trames courantes successives tant que ladite mesure de qualité est supérieure à un seuil de qualité ;
    • sélectionner une nouvelle trame de référence parmi un groupe de trames courantes précédentes si ladite mesure de qualité est inférieure audit seuil de qualité ; et
    • déterminer un champ de mouvement entre la trame courante et ladite nouvelle trame de référence et concaténer ledit champ de mouvement déterminé entre ladite trame courante et ladite nouvelle trame de référence avec un champ de mouvement précédemment généré entre ladite nouvelle trame de référence et ladite trame de référence initialement sélectionnée.
EP15715202.6A 2014-04-02 2015-03-27 Estimation d'un champ de demouvement Not-in-force EP3127087B1 (fr)

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EP14305485.6A EP2927872A1 (fr) 2014-04-02 2014-04-02 Procédé et dispositif de traitement d'une séquence vidéo
PCT/EP2015/056797 WO2015150286A1 (fr) 2014-04-02 2015-03-27 Estimation de champ de mouvement

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US10375422B1 (en) * 2018-03-30 2019-08-06 Tencent America LLC Method and apparatus for motion field based tree splitting
CN111402292B (zh) * 2020-03-10 2023-04-07 南昌航空大学 基于特征变形误差遮挡检测的图像序列光流计算方法
CN111369592B (zh) * 2020-03-13 2023-07-25 浙江工业大学 一种基于牛顿插值的快速全局运动估计方法
US11494881B2 (en) * 2020-12-29 2022-11-08 Hb Innovations, Inc. Global movement image stabilization systems and methods
CN115456998B (zh) * 2022-09-16 2026-02-03 合肥芯明智能科技有限公司 深度图处理方法、装置及存储介质

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US5398068A (en) * 1993-09-02 1995-03-14 Trustees Of Princeton University Method and apparatus for determining motion vectors for image sequences
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WO2012075612A1 (fr) * 2010-12-10 2012-06-14 Technicolor (China) Technology Co., Ltd. Procédé et dispositif pour déterminer un vecteur de mouvement pour un bloc courant d'une trame vidéo courante
WO2013107833A1 (fr) * 2012-01-19 2013-07-25 Thomson Licensing Procédé et dispositif permettant de générer un champ de mouvement pour une séquence vidéo
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EP3127087A1 (fr) 2017-02-08
US20170214935A1 (en) 2017-07-27
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EP2927872A1 (fr) 2015-10-07
WO2015150286A1 (fr) 2015-10-08
CN106416244A (zh) 2017-02-15

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